The present invention pertains to knowledge-based systems and more particularly pertains to a scaleable investment tools and service for aiding users in making investment decisions based upon fundamental requirments of the individuals wishing to make the financial investments.
Investors often work alone or with investment managers to create an investment portfolio that in theory will provide a high return on investment consistent with a degree of risk that the individual investor is willing to take. In order to achieve what is an optimum investment portfolio, research is performed on fundamentals of particulars stock or on industry sectors, historical price data, price-to-earnings ratios, growth rates and so on. There is currently a very large amount of information available, for instance through the Internet, that aids investors in their task.
Barrons frequently lists what it considers to be the top ten websites for investors (e.g., moneycentral.com, quote.yahoo.com, cbs.marketwatch.com, etc.) all of which provide significant research data and investment tools for investors. What this website exemplifies is a principal problem facing today's investors. Investors using these tools quickly discover that the amount of investment data available today is overwhelming and cannot easily be interpreted or digested even by the more expert investor.
The number of potential investment options available to today's investors is equally daunting. Potential investment products include domestic and foreign stocks, mutual finds, stock options, futures, commodities commodity options, options, real estate finds, real estate investment trusts, currency funds, Treasury instruments, corporate and municipal bonds, futures contracts. etc. Generally, specialized knowledge is required in order to maximize profits when selecting investments as well as when timing purchases and sales. This usually gets into trading strategies and market patterns of which there are probably more opinions on than there are stocks to trade.
Computer systems have been used to aid in making investment decisions. One of their advantages is their ability to manipulate large amounts of numerical data over a relatively short period of time. For example, U.S. Pat. No. 5,761,442 describes using a predictive neural network for selecting a portfolio of securities. Each network is trained using available historical data relating to a corresponding security that is deemed to be “appropriate” by the person operating the system. Other experts in the field have tried applying artificial intelligence to the problem of manipulating financial data, for example, see R. Tripp and J. Lee “Artificial Intelligence in Finance and Investing,” 1996.
Bayesian theory is also well suited to predicting outcomes given particular probabilistic data about the factors affecting the desired outcomes. In scientific literature Bayesian networks are referred to by various names: Bayes nets, causal probabilistic networks, Bayesian belief networks or simply belief networks. Loosely defined Bayesian networks are a concise (acyclic) graphical structure for modeling probabilistic relationships among discrete random variables. Bayesian networks are used to efficiently model problem domains containing uncertainty in some manner and therein lies their utility. Since they can be easily modeled on a computer, they are the subject of increasing interest and use in automated decision—support systems, whether for medical diagnosis, automated automotive troubleshooting, or in other areas as mundane as predicting a computer user's likely requirements.
In general, a Bayesian network consists of a set of nodes representing discrete—valued variables connected by arcs representing the causal dependencies between the nodes. A set of conditional probability tables, one for each node, defines the dependency between the nodes and its parents. And, nodes without parents, sometimes called source nodes, have associated therewith a prior marginal probability table. For specific applications the data for the probability tables for all other nodes are provided by what is termed domain experts in whatever field is being modeled. This involves assigning prior probabilities for all nodes without parents, and conditional probabilities for all nodes with parents. In diagnostic Bayesian networks nodes can represent causes, or outcomes of actions and questions. In very large diagnostic Bayesian networks, most of the events are very rare with probabilities in the range of 0.001 to 0.000001. But, since a primary goal of a computer decision support system is to provide likely outcomes of certain actions as accurate as is possible, it is imperative that the domain experts provide probabilistic information that is highly reliable and their best estimate of the situation.
Bayesian networks provide a way to model problem areas using probability theory. The Bayesian network representation of a problem can be used to provide information on a subset of variables given information on others. A Bayesian network consists of a set of variables (nodes) and a set of directed edges (connections between variables). Each variable has a set of mutually exclusive states. The variables together with the directed edges form a directed acyclic graph (DAG). For each variable υ with parents w1, . . . , wn, there is defined a conditional probability table P(υ|w1, . . . , wn). Obviously, if v has no parents, this table reduces to the marginal probability P(υ).
Bayesian networks have been used in many application domains with uncertainty, such as medical diagnosis, pedigree analysis, planning, debt detection, bottleneck detection, etc. However, one of the major application areas not heretofore studied is financial investment since Bayesian networks are well suited to decision support. Financial/investment decision support analysis lends itself nicely to the modeling techniques of Bayesian networks.